Data Preprocessing in Machine Learning | Full Tutorial for Beginners Phase 1
In this video, we start Phase 1 of our Machine Learning Roadmap: Data Preprocessing. Before training any ML model, your data needs to be cleaned, split, scaled, and encoded — and in this tutorial, I break down every concept you need, with visuals, diagrams, and real Python code.
slide link: https://drive.google.com/file/d/1dnqV...
🔹 What you'll learn in this video:
Dataset, Features & Labels — the basic ML vocabulary
Train/Test/Validation Split — and why it matters
Handling Missing Values & Duplicate Values
Detecting and removing Outliers (IQR method)
Feature Scaling: Normalization vs Standardization
Encoding categorical data: One-Hot, Label & Ordinal Encoding
Feature Engineering vs Feature Selection
Data Leakage — the #1 beginner mistake (and how to avoid it)
Handling Imbalanced Datasets with SMOTE
📌 This is Phase 1 of our complete Machine Learning Course. If you missed the roadmap video, watch it first to see the full course structure: [link to roadmap video]
🐍 All code examples use Python and scikit-learn, so make sure you have it installed if you want to follow along.
👍 If this helped you, like the video, subscribe, and turn on notifications so you don't miss Phase 2: Machine Learning Basics — coming next!
⏱️ Timestamps:
00:00 Introduction
00:33 Dataset, Features & Labels
01:14 Train/Test/Validation Split
02:00 Missing Values
02:41 Duplicate Values
03:15 Outliers
04:00 Feature Scaling
05:05 Encoding Categorical Data
06:06 Feature Engineering & Selection
06:54 Data Leakage
07:40 Data Imbalance & SMOTE
08:35 Recap
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